Vertical Distributions of Aerosol and Cloud Microphysical Properties and the Aerosol Impact on a Continental Cumulus Cloud Based on Aircraft Measurements From the Loess Plateau of China

Based on aircraft measurements of aerosols and continental cumulus clouds made over the Loess Plateau of China (Xinzhou, Shanxi Province) on 30 July 2020, this study focuses on the vertical profiles of microphysical properties of aerosols and cumulus clouds, and use them to study aerosol-cloud interactions. During the study period, the boundary layer was stable with a height ∼1,500 m above sea level. Aerosols in the boundary layer mainly came from local emissions, while aerosols above this layer were mostly dust aerosols transported over long distances. Vertical profiles of aerosols and cloud condensation nuclei were obtained, and aerosol activation ratios at different supersaturation (SS) levels ranged from 0.16 to 0.32 at 0.2% SS and 0.70 to 0.85 at 0.8% SS. A thick cumulus cloud in the development stage was observed from the bottom to the top with the horizontal dimension of 10 km by 7 km, the cloud-base height of 2,450 m (15.8°C), and the cloud-top height of 5,400 m (−3°C). The maximum updraft velocity near the cloud top was 13.45 m s−1, and the maximum downdraft velocity occuring in the upper-middle part of the cloud was 4.44 ms−1. The temperature inside the cloud was higher than the outside, with their difference being positively correlated with the cloud water content. The temperature lapse rate inside the cloud was about −6.5°C km−1. The liquid water content and droplet effective radius (Re) increased with increasing height. The cloud droplet number concentration (Nc) increased first then decreased, peaking in the middle and lower part of the cloud, the average values of Nc and Re were 767.9 cm−3 and 5.17 μm, respectively. The cloud droplet spectrum had a multi-peak distribution, with the first appearing at ∼4.5 μm. SS in the cloud first increased then decreased with height. The maximum SS is ∼0.7% appearing at ∼3,800 m. The conversion rate of intra-cloud aerosols to cloud droplets was between 0.2 and 0.54, with the ratio increasing gradually with increasing height. The cloud droplet spectral dispersion and Nc were positively correlated. The aerosol indirect effect (AIE) was estimated to be 0.245 and 0.16, based on Nc and Re, respectively. The cloud droplet dispersion mainly attenuated the AIE, up to ∼34.7%.

On the lifecycle of a shallow cumulus cloud: Is it a bubble or plume, active or forced?

A cloud’s lifecycle determines how its mass flux translates into cloud cover, thereby setting Earth’s albedo. Here, an attempt is made to quantify the most basic aspects of the lifecycle of a shallow cumulus cloud: the degree to which it is a bubble or plume, and active or forced. Quantitative measures are proposed for these properties, which are then applied to hundreds of shallow cumulus clouds in Oklahoma using data from stereo cameras, a Doppler lidar, and large-eddy simulations. The observed clouds are intermediate between active and forced, but behave more like bubbles than plumes. The simulated clouds, on the other hand, are more active and plume-like, suggesting room for improvement in the modeling of shallow cumulus.

Optically thin clouds in the trades

We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models as well as Large Eddy Simulations commonly underestimate the cloud cover, while estimates from observations largely disagree on the cloud cover in the trades. Currently, trade wind clouds contribute significantly to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well and especially how they change in a future climate we have to know how cloudy it is. In this study we develop a method to quantify the cloud cover from a clear-sky perspective. Using well-known radiative transfer relations we retrieve the clear-sky contribution in high-resolution satellite observations of trade cumulus cloud fields during EUREC4A. Knowing the clear-sky part, we can investigate the remaining cloud-related contributions consisting of areas detected by common cloud masking algorithms and those undetected areas related to optically thin clouds. We find that the cloud-mask cloud cover underestimates the total cloud cover by a factor of 2. Lidar measurements on board the HALO aircraft support our findings by showing a high abundance of optically thin clouds during EUREC4A. Mixing the undetected optically thin clouds into the clear-sky signal can cause an underestimation of the cloud radiative effect of up to −32 %. We further discuss possible artificial correlations in aersol-cloud cover interaction studies that might arise from undetected optically thin clouds. Our analysis suggests that the known underestimation of trade wind cloud cover and simultaneous overestiamtion of cloud brightness in models is even higher than assumed so far.

Observed impact of meso-scale vertical motion on cloudiness

We use estimates of meso-scale vertical velocity and co-located cloud measurements from the second Next-Generation Aircraft Remote Sensing for Validation campaign (NARVAL2) in the tropical North Atlantic to show the observed impact of meso-scale vertical motion on tropical clouds. Our results not only confirm previously untested hypotheses about the role of dynamics being non-negligible in determining cloudiness, but go further to show that at the meso-scale, the dynamics has a more dominant control on cloudiness variability than thermodynamics. A simple mass-flux estimate reveals that meso-scale vertical velocity at the sub-cloud layer top explains much of the variations in peak shallow cumulus cloud fraction. In contrast, we find that thermodynamic cloud-controlling factors, such as humidity and stability, are unable to explain the variations in cloudiness at the meso-scale. Thus, capturing the observed variability of cloudiness may require not only a consideration of thermodynamic factors, but also dynamic ones such as the meso-scale vertical velocity.